Overview

Brought to you by YData

Dataset statistics

Number of variables28
Number of observations4935
Missing cells625
Missing cells (%)0.5%
Duplicate rows44
Duplicate rows (%)0.9%
Total size in memory5.0 MiB
Average record size in memory1.0 KiB

Variable types

Categorical3
Text8
Numeric16
URL1

Alerts

Dataset has 44 (0.9%) duplicate rowsDuplicates
actor_1_facebook_likes is highly overall correlated with actor_2_facebook_likes and 2 other fieldsHigh correlation
actor_2_facebook_likes is highly overall correlated with actor_1_facebook_likes and 2 other fieldsHigh correlation
actor_3_facebook_likes is highly overall correlated with actor_1_facebook_likes and 2 other fieldsHigh correlation
budget is highly overall correlated with gross and 3 other fieldsHigh correlation
cast_total_facebook_likes is highly overall correlated with actor_1_facebook_likes and 2 other fieldsHigh correlation
gross is highly overall correlated with budget and 3 other fieldsHigh correlation
language is highly overall correlated with budgetHigh correlation
num_critic_for_reviews is highly overall correlated with gross and 2 other fieldsHigh correlation
num_user_for_reviews is highly overall correlated with budget and 3 other fieldsHigh correlation
num_voted_users is highly overall correlated with budget and 3 other fieldsHigh correlation
color is highly imbalanced (74.9%) Imbalance
language is highly imbalanced (88.9%) Imbalance
plot_keywords has 140 (2.8%) missing values Missing
aspect_ratio has 307 (6.2%) missing values Missing
budget is highly skewed (γ1 = 49.97483492) Skewed
director_facebook_likes has 905 (18.3%) zeros Zeros
actor_3_facebook_likes has 86 (1.7%) zeros Zeros
gross has 779 (15.8%) zeros Zeros
facenumber_in_poster has 2112 (42.8%) zeros Zeros
budget has 392 (7.9%) zeros Zeros
actor_2_facebook_likes has 53 (1.1%) zeros Zeros
movie_facebook_likes has 2134 (43.2%) zeros Zeros

Reproduction

Analysis started2025-05-08 14:07:14.133256
Analysis finished2025-05-08 14:08:02.102902
Duration47.97 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

color
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing15
Missing (%)0.3%
Memory size300.8 KiB
Color
4714 
Black and White
 
206

Length

Max length16
Median length5
Mean length5.4605691
Min length5

Characters and Unicode

Total characters26866
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowColor
2nd rowColor
3rd rowColor
4th rowColor
5th rowColor

Common Values

ValueCountFrequency (%)
Color 4714
95.5%
Black and White 206
 
4.2%
(Missing) 15
 
0.3%

Length

2025-05-08T10:08:02.228062image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-08T10:08:02.403676image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
color 4714
88.4%
black 206
 
3.9%
and 206
 
3.9%
white 206
 
3.9%

Most occurring characters

ValueCountFrequency (%)
o 9428
35.1%
l 4920
18.3%
C 4714
17.5%
r 4714
17.5%
618
 
2.3%
a 412
 
1.5%
B 206
 
0.8%
c 206
 
0.8%
k 206
 
0.8%
n 206
 
0.8%
Other values (6) 1236
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26866
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 9428
35.1%
l 4920
18.3%
C 4714
17.5%
r 4714
17.5%
618
 
2.3%
a 412
 
1.5%
B 206
 
0.8%
c 206
 
0.8%
k 206
 
0.8%
n 206
 
0.8%
Other values (6) 1236
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26866
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 9428
35.1%
l 4920
18.3%
C 4714
17.5%
r 4714
17.5%
618
 
2.3%
a 412
 
1.5%
B 206
 
0.8%
c 206
 
0.8%
k 206
 
0.8%
n 206
 
0.8%
Other values (6) 1236
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26866
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 9428
35.1%
l 4920
18.3%
C 4714
17.5%
r 4714
17.5%
618
 
2.3%
a 412
 
1.5%
B 206
 
0.8%
c 206
 
0.8%
k 206
 
0.8%
n 206
 
0.8%
Other values (6) 1236
 
4.6%
Distinct2395
Distinct (%)48.5%
Missing0
Missing (%)0.0%
Memory size342.5 KiB
2025-05-08T10:08:02.838087image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length32
Median length24
Mean length13.08308
Min length3

Characters and Unicode

Total characters64565
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1502 ?
Unique (%)30.4%

Sample

1st rowJames Cameron
2nd rowGore Verbinski
3rd rowSam Mendes
4th rowChristopher Nolan
5th rowAndrew Stanton
ValueCountFrequency (%)
john 179
 
1.7%
david 150
 
1.5%
michael 127
 
1.2%
james 87
 
0.8%
peter 85
 
0.8%
robert 84
 
0.8%
paul 81
 
0.8%
richard 80
 
0.8%
scott 65
 
0.6%
lee 58
 
0.6%
Other values (2964) 9269
90.3%
2025-05-08T10:08:03.553225image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6092
 
9.4%
5330
 
8.3%
a 5273
 
8.2%
n 4654
 
7.2%
r 4440
 
6.9%
o 3791
 
5.9%
i 3691
 
5.7%
l 2966
 
4.6%
t 2320
 
3.6%
s 2087
 
3.2%
Other values (66) 23921
37.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 64565
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6092
 
9.4%
5330
 
8.3%
a 5273
 
8.2%
n 4654
 
7.2%
r 4440
 
6.9%
o 3791
 
5.9%
i 3691
 
5.7%
l 2966
 
4.6%
t 2320
 
3.6%
s 2087
 
3.2%
Other values (66) 23921
37.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 64565
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6092
 
9.4%
5330
 
8.3%
a 5273
 
8.2%
n 4654
 
7.2%
r 4440
 
6.9%
o 3791
 
5.9%
i 3691
 
5.7%
l 2966
 
4.6%
t 2320
 
3.6%
s 2087
 
3.2%
Other values (66) 23921
37.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 64565
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6092
 
9.4%
5330
 
8.3%
a 5273
 
8.2%
n 4654
 
7.2%
r 4440
 
6.9%
o 3791
 
5.9%
i 3691
 
5.7%
l 2966
 
4.6%
t 2320
 
3.6%
s 2087
 
3.2%
Other values (66) 23921
37.0%

num_critic_for_reviews
Real number (ℝ)

High correlation 

Distinct528
Distinct (%)10.8%
Missing41
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean142.55231
Minimum1
Maximum813
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2025-05-08T10:08:03.811925image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.65
Q153
median112
Q3197
95-th percentile388.35
Maximum813
Range812
Interquartile range (IQR)144

Descriptive statistics

Standard deviation121.6279
Coefficient of variation (CV)0.85321596
Kurtosis2.8879073
Mean142.55231
Median Absolute Deviation (MAD)68
Skewness1.5079729
Sum697651
Variance14793.347
MonotonicityNot monotonic
2025-05-08T10:08:04.055346image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 38
 
0.8%
81 33
 
0.7%
5 32
 
0.6%
16 32
 
0.6%
10 31
 
0.6%
9 31
 
0.6%
12 31
 
0.6%
8 31
 
0.6%
43 30
 
0.6%
29 29
 
0.6%
Other values (518) 4576
92.7%
(Missing) 41
 
0.8%
ValueCountFrequency (%)
1 38
0.8%
2 23
0.5%
3 19
0.4%
4 25
0.5%
5 32
0.6%
6 26
0.5%
7 20
0.4%
8 31
0.6%
9 31
0.6%
10 31
0.6%
ValueCountFrequency (%)
813 1
< 0.1%
775 1
< 0.1%
765 1
< 0.1%
750 2
< 0.1%
739 1
< 0.1%
738 1
< 0.1%
733 1
< 0.1%
723 1
< 0.1%
712 1
< 0.1%
703 2
< 0.1%

duration
Real number (ℝ)

Distinct172
Distinct (%)3.5%
Missing12
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean108.16738
Minimum7
Maximum330
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2025-05-08T10:08:04.281262image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile83
Q194
median104
Q3118
95-th percentile146
Maximum330
Range323
Interquartile range (IQR)24

Descriptive statistics

Standard deviation22.541217
Coefficient of variation (CV)0.20839201
Kurtosis11.568651
Mean108.16738
Median Absolute Deviation (MAD)11
Skewness2.1550676
Sum532508
Variance508.10648
MonotonicityNot monotonic
2025-05-08T10:08:04.511441image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 161
 
3.3%
100 141
 
2.9%
101 139
 
2.8%
98 135
 
2.7%
97 131
 
2.7%
93 129
 
2.6%
99 124
 
2.5%
95 124
 
2.5%
94 124
 
2.5%
96 113
 
2.3%
Other values (162) 3602
73.0%
ValueCountFrequency (%)
7 1
< 0.1%
14 1
< 0.1%
20 1
< 0.1%
25 1
< 0.1%
34 1
< 0.1%
35 1
< 0.1%
37 1
< 0.1%
41 1
< 0.1%
42 1
< 0.1%
45 2
< 0.1%
ValueCountFrequency (%)
330 1
< 0.1%
325 1
< 0.1%
300 1
< 0.1%
293 1
< 0.1%
289 1
< 0.1%
280 1
< 0.1%
271 1
< 0.1%
270 1
< 0.1%
251 2
< 0.1%
240 2
< 0.1%

director_facebook_likes
Real number (ℝ)

Zeros 

Distinct435
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean687.02371
Minimum0
Maximum23000
Zeros905
Zeros (%)18.3%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2025-05-08T10:08:04.725550image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median49
Q3195
95-th percentile973
Maximum23000
Range23000
Interquartile range (IQR)188

Descriptive statistics

Standard deviation2814.4102
Coefficient of variation (CV)4.0965256
Kurtosis27.230494
Mean687.02371
Median Absolute Deviation (MAD)49
Skewness5.2273713
Sum3390462
Variance7920905
MonotonicityNot monotonic
2025-05-08T10:08:04.945151image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 905
 
18.3%
3 70
 
1.4%
6 66
 
1.3%
7 64
 
1.3%
2 63
 
1.3%
4 60
 
1.2%
11 59
 
1.2%
10 53
 
1.1%
8 52
 
1.1%
5 52
 
1.1%
Other values (425) 3491
70.7%
ValueCountFrequency (%)
0 905
18.3%
2 63
 
1.3%
3 70
 
1.4%
4 60
 
1.2%
5 52
 
1.1%
6 66
 
1.3%
7 64
 
1.3%
8 52
 
1.1%
9 49
 
1.0%
10 53
 
1.1%
ValueCountFrequency (%)
23000 1
 
< 0.1%
22000 8
 
0.2%
21000 10
 
0.2%
20000 1
 
< 0.1%
18000 4
 
0.1%
17000 20
0.4%
16000 28
0.6%
15000 2
 
< 0.1%
14000 30
0.6%
13000 26
0.5%

actor_3_facebook_likes
Real number (ℝ)

High correlation  Zeros 

Distinct906
Distinct (%)18.4%
Missing18
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean651.41936
Minimum0
Maximum23000
Zeros86
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2025-05-08T10:08:05.250529image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q1133
median372
Q3637
95-th percentile1000
Maximum23000
Range23000
Interquartile range (IQR)504

Descriptive statistics

Standard deviation1681.3943
Coefficient of variation (CV)2.5811243
Kurtosis59.29899
Mean651.41936
Median Absolute Deviation (MAD)250
Skewness7.2057705
Sum3203029
Variance2827086.9
MonotonicityNot monotonic
2025-05-08T10:08:05.471575image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 125
 
2.5%
0 86
 
1.7%
11000 29
 
0.6%
3 27
 
0.5%
2000 27
 
0.5%
3000 26
 
0.5%
826 22
 
0.4%
4 21
 
0.4%
7 21
 
0.4%
249 19
 
0.4%
Other values (896) 4514
91.5%
ValueCountFrequency (%)
0 86
1.7%
2 18
 
0.4%
3 27
 
0.5%
4 21
 
0.4%
5 16
 
0.3%
6 17
 
0.3%
7 21
 
0.4%
8 17
 
0.3%
9 15
 
0.3%
10 12
 
0.2%
ValueCountFrequency (%)
23000 2
 
< 0.1%
20000 1
 
< 0.1%
19000 5
 
0.1%
17000 1
 
< 0.1%
16000 3
 
0.1%
15000 1
 
< 0.1%
14000 6
 
0.1%
13000 5
 
0.1%
12000 8
 
0.2%
11000 29
0.6%
Distinct2963
Distinct (%)60.2%
Missing10
Missing (%)0.2%
Memory size341.8 KiB
2025-05-08T10:08:05.855375image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length28
Median length25
Mean length13.064365
Min length3

Characters and Unicode

Total characters64342
Distinct characters78
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2035 ?
Unique (%)41.3%

Sample

1st rowJoel David Moore
2nd rowOrlando Bloom
3rd rowRory Kinnear
4th rowChristian Bale
5th rowSamantha Morton
ValueCountFrequency (%)
michael 100
 
1.0%
david 57
 
0.6%
john 54
 
0.5%
scott 50
 
0.5%
james 50
 
0.5%
tom 49
 
0.5%
robert 44
 
0.4%
jason 43
 
0.4%
kevin 40
 
0.4%
bruce 38
 
0.4%
Other values (3762) 9656
94.8%
2025-05-08T10:08:06.471107image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6091
 
9.5%
a 5812
 
9.0%
5256
 
8.2%
n 4645
 
7.2%
r 4315
 
6.7%
i 3932
 
6.1%
o 3555
 
5.5%
l 3343
 
5.2%
t 2295
 
3.6%
s 2108
 
3.3%
Other values (68) 22990
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 64342
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6091
 
9.5%
a 5812
 
9.0%
5256
 
8.2%
n 4645
 
7.2%
r 4315
 
6.7%
i 3932
 
6.1%
o 3555
 
5.5%
l 3343
 
5.2%
t 2295
 
3.6%
s 2108
 
3.3%
Other values (68) 22990
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 64342
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6091
 
9.5%
a 5812
 
9.0%
5256
 
8.2%
n 4645
 
7.2%
r 4315
 
6.7%
i 3932
 
6.1%
o 3555
 
5.5%
l 3343
 
5.2%
t 2295
 
3.6%
s 2108
 
3.3%
Other values (68) 22990
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 64342
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6091
 
9.5%
a 5812
 
9.0%
5256
 
8.2%
n 4645
 
7.2%
r 4315
 
6.7%
i 3932
 
6.1%
o 3555
 
5.5%
l 3343
 
5.2%
t 2295
 
3.6%
s 2108
 
3.3%
Other values (68) 22990
35.7%

actor_1_facebook_likes
Real number (ℝ)

High correlation 

Distinct866
Distinct (%)17.6%
Missing7
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean6673.3764
Minimum0
Maximum640000
Zeros25
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2025-05-08T10:08:06.712652image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile97.7
Q1618.75
median995
Q311000
95-th percentile24000
Maximum640000
Range640000
Interquartile range (IQR)10381.25

Descriptive statistics

Standard deviation15155.539
Coefficient of variation (CV)2.2710451
Kurtosis673.41703
Mean6673.3764
Median Absolute Deviation (MAD)777
Skewness19.002396
Sum32886399
Variance2.2969035 × 108
MonotonicityNot monotonic
2025-05-08T10:08:06.934612image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 435
 
8.8%
11000 210
 
4.3%
2000 195
 
4.0%
3000 153
 
3.1%
12000 135
 
2.7%
13000 127
 
2.6%
14000 123
 
2.5%
10000 111
 
2.2%
18000 109
 
2.2%
22000 82
 
1.7%
Other values (856) 3248
65.8%
ValueCountFrequency (%)
0 25
0.5%
2 8
 
0.2%
3 4
 
0.1%
4 2
 
< 0.1%
5 6
 
0.1%
6 3
 
0.1%
7 3
 
0.1%
8 1
 
< 0.1%
9 3
 
0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
640000 1
 
< 0.1%
260000 3
 
0.1%
164000 2
 
< 0.1%
137000 2
 
< 0.1%
87000 8
 
0.2%
77000 1
 
< 0.1%
49000 27
0.5%
46000 1
 
< 0.1%
45000 5
 
0.1%
44000 2
 
< 0.1%

gross
Real number (ℝ)

High correlation  Zeros 

Distinct4034
Distinct (%)81.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40846822
Minimum0
Maximum7.6050585 × 108
Zeros779
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2025-05-08T10:08:07.174073image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1482267.5
median16284360
Q352446759
95-th percentile1.6774907 × 108
Maximum7.6050585 × 108
Range7.6050585 × 108
Interquartile range (IQR)51964492

Descriptive statistics

Standard deviation65270489
Coefficient of variation (CV)1.5979331
Kurtosis16.734751
Mean40846822
Median Absolute Deviation (MAD)16284360
Skewness3.3273435
Sum2.0157907 × 1011
Variance4.2602367 × 1015
MonotonicityNot monotonic
2025-05-08T10:08:07.412736image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 779
 
15.8%
8000000 3
 
0.1%
177343675 3
 
0.1%
218051260 3
 
0.1%
3000000 3
 
0.1%
47000000 3
 
0.1%
34964818 3
 
0.1%
5773519 3
 
0.1%
144512310 3
 
0.1%
12189514 2
 
< 0.1%
Other values (4024) 4130
83.7%
ValueCountFrequency (%)
0 779
15.8%
162 1
 
< 0.1%
703 1
 
< 0.1%
721 1
 
< 0.1%
728 1
 
< 0.1%
828 1
 
< 0.1%
1111 1
 
< 0.1%
1332 1
 
< 0.1%
1521 1
 
< 0.1%
1711 1
 
< 0.1%
ValueCountFrequency (%)
760505847 1
< 0.1%
658672302 1
< 0.1%
652177271 1
< 0.1%
623279547 2
< 0.1%
533316061 1
< 0.1%
474544677 1
< 0.1%
460935665 1
< 0.1%
458991599 1
< 0.1%
448130642 1
< 0.1%
436471036 1
< 0.1%

genres
Text

Distinct899
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size372.7 KiB
2025-05-08T10:08:07.697840image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length64
Median length53
Mean length20.315299
Min length5

Characters and Unicode

Total characters100256
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique488 ?
Unique (%)9.9%

Sample

1st rowaction|adventure|fantasy|sci-fi
2nd rowaction|adventure|fantasy
3rd rowaction|adventure|thriller
4th rowaction|thriller
5th rowaction|adventure|sci-fi
ValueCountFrequency (%)
drama 232
 
4.7%
comedy 200
 
4.1%
comedy|drama 189
 
3.8%
comedy|drama|romance 184
 
3.7%
comedy|romance 154
 
3.1%
drama|romance 149
 
3.0%
crime|drama|thriller 98
 
2.0%
horror 71
 
1.4%
action|crime|thriller 65
 
1.3%
action|crime|drama|thriller 64
 
1.3%
Other values (889) 3529
71.5%
2025-05-08T10:08:08.281328image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 11405
11.4%
a 11119
11.1%
| 9252
 
9.2%
m 8015
 
8.0%
e 7782
 
7.8%
i 6429
 
6.4%
o 6216
 
6.2%
c 5979
 
6.0%
d 5385
 
5.4%
t 5341
 
5.3%
Other values (13) 23333
23.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100256
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 11405
11.4%
a 11119
11.1%
| 9252
 
9.2%
m 8015
 
8.0%
e 7782
 
7.8%
i 6429
 
6.4%
o 6216
 
6.2%
c 5979
 
6.0%
d 5385
 
5.4%
t 5341
 
5.3%
Other values (13) 23333
23.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100256
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 11405
11.4%
a 11119
11.1%
| 9252
 
9.2%
m 8015
 
8.0%
e 7782
 
7.8%
i 6429
 
6.4%
o 6216
 
6.2%
c 5979
 
6.0%
d 5385
 
5.4%
t 5341
 
5.3%
Other values (13) 23333
23.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100256
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 11405
11.4%
a 11119
11.1%
| 9252
 
9.2%
m 8015
 
8.0%
e 7782
 
7.8%
i 6429
 
6.4%
o 6216
 
6.2%
c 5979
 
6.0%
d 5385
 
5.4%
t 5341
 
5.3%
Other values (13) 23333
23.3%
Distinct2043
Distinct (%)41.5%
Missing7
Missing (%)0.1%
Memory size341.0 KiB
2025-05-08T10:08:08.788942image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length27
Median length24
Mean length13.186891
Min length4

Characters and Unicode

Total characters64985
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1326 ?
Unique (%)26.9%

Sample

1st rowCCH Pounder
2nd rowJohnny Depp
3rd rowChristoph Waltz
4th rowTom Hardy
5th rowDaryl Sabara
ValueCountFrequency (%)
robert 109
 
1.1%
tom 92
 
0.9%
michael 88
 
0.9%
jason 57
 
0.6%
de 57
 
0.6%
james 52
 
0.5%
steve 50
 
0.5%
bruce 50
 
0.5%
jr 49
 
0.5%
niro 49
 
0.5%
Other values (2826) 9564
93.6%
2025-05-08T10:08:09.490592image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6084
 
9.4%
a 5610
 
8.6%
5289
 
8.1%
n 4712
 
7.3%
r 4206
 
6.5%
i 4163
 
6.4%
o 3826
 
5.9%
l 3240
 
5.0%
t 2521
 
3.9%
s 2300
 
3.5%
Other values (66) 23034
35.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 64985
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6084
 
9.4%
a 5610
 
8.6%
5289
 
8.1%
n 4712
 
7.3%
r 4206
 
6.5%
i 4163
 
6.4%
o 3826
 
5.9%
l 3240
 
5.0%
t 2521
 
3.9%
s 2300
 
3.5%
Other values (66) 23034
35.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 64985
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6084
 
9.4%
a 5610
 
8.6%
5289
 
8.1%
n 4712
 
7.3%
r 4206
 
6.5%
i 4163
 
6.4%
o 3826
 
5.9%
l 3240
 
5.0%
t 2521
 
3.9%
s 2300
 
3.5%
Other values (66) 23034
35.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 64985
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6084
 
9.4%
a 5610
 
8.6%
5289
 
8.1%
n 4712
 
7.3%
r 4206
 
6.5%
i 4163
 
6.4%
o 3826
 
5.9%
l 3240
 
5.0%
t 2521
 
3.9%
s 2300
 
3.5%
Other values (66) 23034
35.4%
Distinct4811
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size557.7 KiB
2025-05-08T10:08:09.930513image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length87
Median length59
Mean length16.340426
Min length2

Characters and Unicode

Total characters80640
Distinct characters96
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4694 ?
Unique (%)95.1%

Sample

1st rowAvatar 
2nd rowPirates of the Caribbean: At World's End 
3rd rowSpectre 
4th rowThe Dark Knight Rises 
5th rowJohn Carter 
ValueCountFrequency (%)
the 1571
 
11.5%
of 477
 
3.5%
a 191
 
1.4%
and 146
 
1.1%
in 121
 
0.9%
to 107
 
0.8%
2 104
 
0.8%
78
 
0.6%
man 65
 
0.5%
love 56
 
0.4%
Other values (4832) 10798
78.7%
2025-05-08T10:08:10.645198image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8779
 
10.9%
e 7727
 
9.6%
  4935
 
6.1%
a 4765
 
5.9%
o 4597
 
5.7%
n 4057
 
5.0%
r 4054
 
5.0%
i 3868
 
4.8%
t 3752
 
4.7%
s 2955
 
3.7%
Other values (86) 31151
38.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 80640
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8779
 
10.9%
e 7727
 
9.6%
  4935
 
6.1%
a 4765
 
5.9%
o 4597
 
5.7%
n 4057
 
5.0%
r 4054
 
5.0%
i 3868
 
4.8%
t 3752
 
4.7%
s 2955
 
3.7%
Other values (86) 31151
38.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 80640
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8779
 
10.9%
e 7727
 
9.6%
  4935
 
6.1%
a 4765
 
5.9%
o 4597
 
5.7%
n 4057
 
5.0%
r 4054
 
5.0%
i 3868
 
4.8%
t 3752
 
4.7%
s 2955
 
3.7%
Other values (86) 31151
38.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 80640
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8779
 
10.9%
e 7727
 
9.6%
  4935
 
6.1%
a 4765
 
5.9%
o 4597
 
5.7%
n 4057
 
5.0%
r 4054
 
5.0%
i 3868
 
4.8%
t 3752
 
4.7%
s 2955
 
3.7%
Other values (86) 31151
38.6%

num_voted_users
Real number (ℝ)

High correlation 

Distinct4729
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84864.593
Minimum5
Maximum1689764
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2025-05-08T10:08:10.890948image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile533.1
Q18946
median34985
Q397888
95-th percentile336790.6
Maximum1689764
Range1689759
Interquartile range (IQR)88942

Descriptive statistics

Standard deviation139601.59
Coefficient of variation (CV)1.6449921
Kurtosis24.021176
Mean84864.593
Median Absolute Deviation (MAD)31361
Skewness3.9965954
Sum4.1880677 × 108
Variance1.9488603 × 1010
MonotonicityNot monotonic
2025-05-08T10:08:11.208972image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57 5
 
0.1%
3119 3
 
0.1%
374 3
 
0.1%
6 3
 
0.1%
53 3
 
0.1%
38 3
 
0.1%
6025 3
 
0.1%
162 3
 
0.1%
2541 3
 
0.1%
62 3
 
0.1%
Other values (4719) 4903
99.4%
ValueCountFrequency (%)
5 2
< 0.1%
6 3
0.1%
7 2
< 0.1%
8 2
< 0.1%
13 1
 
< 0.1%
15 2
< 0.1%
16 1
 
< 0.1%
18 1
 
< 0.1%
19 1
 
< 0.1%
22 2
< 0.1%
ValueCountFrequency (%)
1689764 1
< 0.1%
1676169 1
< 0.1%
1468200 1
< 0.1%
1347461 1
< 0.1%
1324680 1
< 0.1%
1251222 1
< 0.1%
1238746 1
< 0.1%
1217752 1
< 0.1%
1215718 1
< 0.1%
1155770 1
< 0.1%

cast_total_facebook_likes
Real number (ℝ)

High correlation 

Distinct3926
Distinct (%)79.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9847.6881
Minimum0
Maximum656730
Zeros32
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2025-05-08T10:08:11.503887image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile192
Q11427.5
median3132
Q314026.5
95-th percentile37340.8
Maximum656730
Range656730
Interquartile range (IQR)12599

Descriptive statistics

Standard deviation18322.496
Coefficient of variation (CV)1.8605885
Kurtosis356.09259
Mean9847.6881
Median Absolute Deviation (MAD)2356
Skewness12.752069
Sum48598341
Variance3.3571385 × 108
MonotonicityNot monotonic
2025-05-08T10:08:11.836038image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32
 
0.6%
5 6
 
0.1%
2 6
 
0.1%
2020 6
 
0.1%
673 5
 
0.1%
29 5
 
0.1%
1044 5
 
0.1%
2730 4
 
0.1%
1752 4
 
0.1%
1440 4
 
0.1%
Other values (3916) 4858
98.4%
ValueCountFrequency (%)
0 32
0.6%
2 6
 
0.1%
3 1
 
< 0.1%
4 2
 
< 0.1%
5 6
 
0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
656730 1
< 0.1%
303717 1
< 0.1%
283939 1
< 0.1%
263584 1
< 0.1%
261818 1
< 0.1%
170118 1
< 0.1%
140268 1
< 0.1%
137712 1
< 0.1%
120797 1
< 0.1%
108016 1
< 0.1%
Distinct3451
Distinct (%)70.2%
Missing18
Missing (%)0.4%
Memory size342.0 KiB
2025-05-08T10:08:12.216952image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length29
Median length25
Mean length13.077893
Min length3

Characters and Unicode

Total characters64304
Distinct characters81
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2599 ?
Unique (%)52.9%

Sample

1st rowWes Studi
2nd rowJack Davenport
3rd rowStephanie Sigman
4th rowJoseph Gordon-Levitt
5th rowPolly Walker
ValueCountFrequency (%)
michael 86
 
0.8%
john 77
 
0.8%
david 69
 
0.7%
james 66
 
0.6%
robert 46
 
0.5%
paul 40
 
0.4%
tom 40
 
0.4%
kevin 39
 
0.4%
peter 38
 
0.4%
steve 36
 
0.4%
Other values (4245) 9643
94.7%
2025-05-08T10:08:12.838144image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6072
 
9.4%
a 5875
 
9.1%
5263
 
8.2%
n 4487
 
7.0%
r 4094
 
6.4%
i 3902
 
6.1%
o 3493
 
5.4%
l 3433
 
5.3%
t 2309
 
3.6%
s 2298
 
3.6%
Other values (71) 23078
35.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 64304
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6072
 
9.4%
a 5875
 
9.1%
5263
 
8.2%
n 4487
 
7.0%
r 4094
 
6.4%
i 3902
 
6.1%
o 3493
 
5.4%
l 3433
 
5.3%
t 2309
 
3.6%
s 2298
 
3.6%
Other values (71) 23078
35.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 64304
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6072
 
9.4%
a 5875
 
9.1%
5263
 
8.2%
n 4487
 
7.0%
r 4094
 
6.4%
i 3902
 
6.1%
o 3493
 
5.4%
l 3433
 
5.3%
t 2309
 
3.6%
s 2298
 
3.6%
Other values (71) 23078
35.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 64304
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6072
 
9.4%
a 5875
 
9.1%
5263
 
8.2%
n 4487
 
7.0%
r 4094
 
6.4%
i 3902
 
6.1%
o 3493
 
5.4%
l 3433
 
5.3%
t 2309
 
3.6%
s 2298
 
3.6%
Other values (71) 23078
35.9%

facenumber_in_poster
Real number (ℝ)

Zeros 

Distinct19
Distinct (%)0.4%
Missing13
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1.3648923
Minimum0
Maximum43
Zeros2112
Zeros (%)42.8%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2025-05-08T10:08:13.050578image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum43
Range43
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0132993
Coefficient of variation (CV)1.4750609
Kurtosis53.19162
Mean1.3648923
Median Absolute Deviation (MAD)1
Skewness4.4486709
Sum6718
Variance4.053374
MonotonicityNot monotonic
2025-05-08T10:08:13.226212image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 2112
42.8%
1 1225
24.8%
2 700
 
14.2%
3 373
 
7.6%
4 198
 
4.0%
5 109
 
2.2%
6 75
 
1.5%
7 48
 
1.0%
8 34
 
0.7%
9 17
 
0.3%
Other values (9) 31
 
0.6%
(Missing) 13
 
0.3%
ValueCountFrequency (%)
0 2112
42.8%
1 1225
24.8%
2 700
 
14.2%
3 373
 
7.6%
4 198
 
4.0%
5 109
 
2.2%
6 75
 
1.5%
7 48
 
1.0%
8 34
 
0.7%
9 17
 
0.3%
ValueCountFrequency (%)
43 1
 
< 0.1%
31 1
 
< 0.1%
19 1
 
< 0.1%
15 6
 
0.1%
14 1
 
< 0.1%
13 2
 
< 0.1%
12 4
 
0.1%
11 5
 
0.1%
10 10
0.2%
9 17
0.3%

plot_keywords
Text

Missing 

Distinct4667
Distinct (%)97.3%
Missing140
Missing (%)2.8%
Memory size516.5 KiB
2025-05-08T10:08:13.602186image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length149
Median length102
Mean length52.340355
Min length2

Characters and Unicode

Total characters250972
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4548 ?
Unique (%)94.8%

Sample

1st rowavatar|future|marine|native|paraplegic
2nd rowgoddess|marriage ceremony|marriage proposal|pirate|singapore
3rd rowbomb|espionage|sequel|spy|terrorist
4th rowdeception|imprisonment|lawlessness|police officer|terrorist plot
5th rowalien|american civil war|male nipple|mars|princess
ValueCountFrequency (%)
in 323
 
1.8%
of 217
 
1.2%
on 201
 
1.1%
the 188
 
1.1%
a 182
 
1.0%
to 179
 
1.0%
york 120
 
0.7%
female 102
 
0.6%
based 101
 
0.6%
by 99
 
0.6%
Other values (11252) 15882
90.3%
2025-05-08T10:08:14.269473image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 24286
 
9.7%
a 19177
 
7.6%
| 18848
 
7.5%
i 18314
 
7.3%
r 17751
 
7.1%
t 15835
 
6.3%
n 15357
 
6.1%
o 15140
 
6.0%
s 13013
 
5.2%
12799
 
5.1%
Other values (32) 80452
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 250972
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 24286
 
9.7%
a 19177
 
7.6%
| 18848
 
7.5%
i 18314
 
7.3%
r 17751
 
7.1%
t 15835
 
6.3%
n 15357
 
6.1%
o 15140
 
6.0%
s 13013
 
5.2%
12799
 
5.1%
Other values (32) 80452
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 250972
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 24286
 
9.7%
a 19177
 
7.6%
| 18848
 
7.5%
i 18314
 
7.3%
r 17751
 
7.1%
t 15835
 
6.3%
n 15357
 
6.1%
o 15140
 
6.0%
s 13013
 
5.2%
12799
 
5.1%
Other values (32) 80452
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 250972
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 24286
 
9.7%
a 19177
 
7.6%
| 18848
 
7.5%
i 18314
 
7.3%
r 17751
 
7.1%
t 15835
 
6.3%
n 15357
 
6.1%
o 15140
 
6.0%
s 13013
 
5.2%
12799
 
5.1%
Other values (32) 80452
32.1%
Distinct4813
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size525.4 KiB
http://www.imdb.com/title/tt2224026/?ref_=fn_tt_tt_1
 
3
http://www.imdb.com/title/tt0077651/?ref_=fn_tt_tt_1
 
3
http://www.imdb.com/title/tt0360717/?ref_=fn_tt_tt_1
 
3
http://www.imdb.com/title/tt1976009/?ref_=fn_tt_tt_1
 
3
http://www.imdb.com/title/tt0232500/?ref_=fn_tt_tt_1
 
3
Other values (4808)
4920 
ValueCountFrequency (%)
http://www.imdb.com/title/tt2224026/?ref_=fn_tt_tt_1 3
 
0.1%
http://www.imdb.com/title/tt0077651/?ref_=fn_tt_tt_1 3
 
0.1%
http://www.imdb.com/title/tt0360717/?ref_=fn_tt_tt_1 3
 
0.1%
http://www.imdb.com/title/tt1976009/?ref_=fn_tt_tt_1 3
 
0.1%
http://www.imdb.com/title/tt0232500/?ref_=fn_tt_tt_1 3
 
0.1%
http://www.imdb.com/title/tt2638144/?ref_=fn_tt_tt_1 3
 
0.1%
http://www.imdb.com/title/tt3332064/?ref_=fn_tt_tt_1 3
 
0.1%
http://www.imdb.com/title/tt0399201/?ref_=fn_tt_tt_1 2
 
< 0.1%
http://www.imdb.com/title/tt0120749/?ref_=fn_tt_tt_1 2
 
< 0.1%
http://www.imdb.com/title/tt0056193/?ref_=fn_tt_tt_1 2
 
< 0.1%
Other values (4803) 4908
99.5%
ValueCountFrequency (%)
http 4935
100.0%
ValueCountFrequency (%)
www.imdb.com 4935
100.0%
ValueCountFrequency (%)
/title/tt2224026/ 3
 
0.1%
/title/tt0077651/ 3
 
0.1%
/title/tt0360717/ 3
 
0.1%
/title/tt1976009/ 3
 
0.1%
/title/tt0232500/ 3
 
0.1%
/title/tt2638144/ 3
 
0.1%
/title/tt3332064/ 3
 
0.1%
/title/tt0399201/ 2
 
< 0.1%
/title/tt0120749/ 2
 
< 0.1%
/title/tt0056193/ 2
 
< 0.1%
Other values (4803) 4908
99.5%
ValueCountFrequency (%)
ref_=fn_tt_tt_1 4935
100.0%
ValueCountFrequency (%)
4935
100.0%

num_user_for_reviews
Real number (ℝ)

High correlation 

Distinct954
Distinct (%)19.4%
Missing15
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean276.55264
Minimum1
Maximum5060
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2025-05-08T10:08:14.542818image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q167
median159
Q3331
95-th percentile916
Maximum5060
Range5059
Interquartile range (IQR)264

Descriptive statistics

Standard deviation380.66199
Coefficient of variation (CV)1.376454
Kurtosis26.046533
Mean276.55264
Median Absolute Deviation (MAD)114
Skewness4.0923538
Sum1360639
Variance144903.55
MonotonicityNot monotonic
2025-05-08T10:08:14.792627image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 50
 
1.0%
3 33
 
0.7%
2 31
 
0.6%
26 31
 
0.6%
10 29
 
0.6%
6 26
 
0.5%
8 25
 
0.5%
50 25
 
0.5%
32 25
 
0.5%
11 23
 
0.5%
Other values (944) 4622
93.7%
ValueCountFrequency (%)
1 50
1.0%
2 31
0.6%
3 33
0.7%
4 22
0.4%
5 19
 
0.4%
6 26
0.5%
7 17
 
0.3%
8 25
0.5%
9 22
0.4%
10 29
0.6%
ValueCountFrequency (%)
5060 1
< 0.1%
4667 1
< 0.1%
4144 1
< 0.1%
3646 1
< 0.1%
3597 1
< 0.1%
3516 1
< 0.1%
3400 1
< 0.1%
3286 1
< 0.1%
3189 1
< 0.1%
3054 1
< 0.1%

language
Categorical

High correlation  Imbalance 

Distinct46
Distinct (%)0.9%
Missing11
Missing (%)0.2%
Memory size308.2 KiB
english
4607 
french
 
72
spanish
 
40
hindi
 
28
mandarin
 
26
Other values (41)
 
151

Length

Max length10
Median length7
Mean length6.9817222
Min length4

Characters and Unicode

Total characters34378
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)0.4%

Sample

1st rowenglish
2nd rowenglish
3rd rowenglish
4th rowenglish
5th rowenglish

Common Values

ValueCountFrequency (%)
english 4607
93.4%
french 72
 
1.5%
spanish 40
 
0.8%
hindi 28
 
0.6%
mandarin 26
 
0.5%
german 19
 
0.4%
japanese 17
 
0.3%
cantonese 11
 
0.2%
russian 11
 
0.2%
italian 10
 
0.2%
Other values (36) 83
 
1.7%
(Missing) 11
 
0.2%

Length

2025-05-08T10:08:15.026298image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
english 4607
93.6%
french 72
 
1.5%
spanish 40
 
0.8%
hindi 28
 
0.6%
mandarin 26
 
0.5%
german 19
 
0.4%
japanese 17
 
0.3%
cantonese 11
 
0.2%
russian 11
 
0.2%
italian 10
 
0.2%
Other values (36) 83
 
1.7%

Most occurring characters

ValueCountFrequency (%)
n 4933
14.3%
e 4817
14.0%
i 4816
14.0%
h 4777
13.9%
s 4772
13.9%
g 4645
13.5%
l 4629
13.5%
a 255
 
0.7%
r 172
 
0.5%
c 100
 
0.3%
Other values (14) 462
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34378
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 4933
14.3%
e 4817
14.0%
i 4816
14.0%
h 4777
13.9%
s 4772
13.9%
g 4645
13.5%
l 4629
13.5%
a 255
 
0.7%
r 172
 
0.5%
c 100
 
0.3%
Other values (14) 462
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34378
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 4933
14.3%
e 4817
14.0%
i 4816
14.0%
h 4777
13.9%
s 4772
13.9%
g 4645
13.5%
l 4629
13.5%
a 255
 
0.7%
r 172
 
0.5%
c 100
 
0.3%
Other values (14) 462
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34378
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 4933
14.3%
e 4817
14.0%
i 4816
14.0%
h 4777
13.9%
s 4772
13.9%
g 4645
13.5%
l 4629
13.5%
a 255
 
0.7%
r 172
 
0.5%
c 100
 
0.3%
Other values (14) 462
 
1.3%
Distinct65
Distinct (%)1.3%
Missing1
Missing (%)< 0.1%
Memory size291.6 KiB
2025-05-08T10:08:15.258302image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length20
Median length3
Mean length3.4916903
Min length2

Characters and Unicode

Total characters17228
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)0.6%

Sample

1st rowusa
2nd rowusa
3rd rowuk
4th rowusa
5th rowusa
ValueCountFrequency (%)
usa 3733
74.7%
uk 434
 
8.7%
france 152
 
3.0%
canada 122
 
2.4%
germany 100
 
2.0%
australia 53
 
1.1%
india 34
 
0.7%
spain 33
 
0.7%
china 30
 
0.6%
south 22
 
0.4%
Other values (63) 287
 
5.7%
2025-05-08T10:08:15.756904image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 4868
28.3%
u 4269
24.8%
s 3894
22.6%
n 655
 
3.8%
k 481
 
2.8%
r 418
 
2.4%
e 407
 
2.4%
c 349
 
2.0%
i 326
 
1.9%
d 221
 
1.3%
Other values (16) 1340
 
7.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17228
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4868
28.3%
u 4269
24.8%
s 3894
22.6%
n 655
 
3.8%
k 481
 
2.8%
r 418
 
2.4%
e 407
 
2.4%
c 349
 
2.0%
i 326
 
1.9%
d 221
 
1.3%
Other values (16) 1340
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17228
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4868
28.3%
u 4269
24.8%
s 3894
22.6%
n 655
 
3.8%
k 481
 
2.8%
r 418
 
2.4%
e 407
 
2.4%
c 349
 
2.0%
i 326
 
1.9%
d 221
 
1.3%
Other values (16) 1340
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17228
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4868
28.3%
u 4269
24.8%
s 3894
22.6%
n 655
 
3.8%
k 481
 
2.8%
r 418
 
2.4%
e 407
 
2.4%
c 349
 
2.0%
i 326
 
1.9%
d 221
 
1.3%
Other values (16) 1340
 
7.8%

content_rating
Categorical

Distinct15
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size289.8 KiB
R
2118 
PG-13
1461 
PG
701 
Not Rated
376 
G
 
112
Other values (10)
 
167

Length

Max length9
Median length8
Mean length3.112462
Min length1

Characters and Unicode

Total characters15360
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPG-13
2nd rowPG-13
3rd rowPG-13
4th rowPG-13
5th rowPG-13

Common Values

ValueCountFrequency (%)
R 2118
42.9%
PG-13 1461
29.6%
PG 701
 
14.2%
Not Rated 376
 
7.6%
G 112
 
2.3%
Unrated 62
 
1.3%
Approved 55
 
1.1%
X 13
 
0.3%
Passed 9
 
0.2%
NC-17 7
 
0.1%
Other values (5) 21
 
0.4%

Length

2025-05-08T10:08:16.007327image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
r 2118
39.9%
pg-13 1461
27.5%
pg 701
 
13.2%
not 376
 
7.1%
rated 376
 
7.1%
g 112
 
2.1%
unrated 62
 
1.2%
approved 55
 
1.0%
x 13
 
0.2%
passed 9
 
0.2%
Other values (6) 28
 
0.5%

Most occurring characters

ValueCountFrequency (%)
R 2494
16.2%
G 2287
14.9%
P 2180
14.2%
- 1478
9.6%
1 1471
9.6%
3 1461
9.5%
t 814
 
5.3%
e 502
 
3.3%
d 502
 
3.3%
a 447
 
2.9%
Other values (17) 1724
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15360
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 2494
16.2%
G 2287
14.9%
P 2180
14.2%
- 1478
9.6%
1 1471
9.6%
3 1461
9.5%
t 814
 
5.3%
e 502
 
3.3%
d 502
 
3.3%
a 447
 
2.9%
Other values (17) 1724
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15360
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 2494
16.2%
G 2287
14.9%
P 2180
14.2%
- 1478
9.6%
1 1471
9.6%
3 1461
9.5%
t 814
 
5.3%
e 502
 
3.3%
d 502
 
3.3%
a 447
 
2.9%
Other values (17) 1724
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15360
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 2494
16.2%
G 2287
14.9%
P 2180
14.2%
- 1478
9.6%
1 1471
9.6%
3 1461
9.5%
t 814
 
5.3%
e 502
 
3.3%
d 502
 
3.3%
a 447
 
2.9%
Other values (17) 1724
11.2%

budget
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct440
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36646196
Minimum0
Maximum1.22155 × 1010
Zeros392
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2025-05-08T10:08:16.238645image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13500079.5
median16000000
Q340000000
95-th percentile1.25 × 108
Maximum1.22155 × 1010
Range1.22155 × 1010
Interquartile range (IQR)36499920

Descriptive statistics

Standard deviation1.9821978 × 108
Coefficient of variation (CV)5.4090139
Kurtosis2940.1074
Mean36646196
Median Absolute Deviation (MAD)14400000
Skewness49.974835
Sum1.8084898 × 1011
Variance3.9291082 × 1016
MonotonicityNot monotonic
2025-05-08T10:08:16.478249image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 392
 
7.9%
20000000 174
 
3.5%
25000000 142
 
2.9%
15000000 142
 
2.9%
30000000 141
 
2.9%
10000000 135
 
2.7%
40000000 131
 
2.7%
35000000 120
 
2.4%
5000000 110
 
2.2%
50000000 101
 
2.0%
Other values (430) 3347
67.8%
ValueCountFrequency (%)
0 392
7.9%
218 1
 
< 0.1%
1100 1
 
< 0.1%
1400 1
 
< 0.1%
3250 1
 
< 0.1%
4500 1
 
< 0.1%
7000 3
 
0.1%
9000 1
 
< 0.1%
10000 3
 
0.1%
13000 1
 
< 0.1%
ValueCountFrequency (%)
1.22155 × 10101
< 0.1%
4200000000 1
< 0.1%
2500000000 1
< 0.1%
2400000000 1
< 0.1%
2127519898 1
< 0.1%
1100000000 1
< 0.1%
1000000000 1
< 0.1%
700000000 2
< 0.1%
600000000 1
< 0.1%
553632000 1
< 0.1%

title_year
Real number (ℝ)

Distinct91
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2002.4705
Minimum1916
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2025-05-08T10:08:16.714073image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1916
5-th percentile1979
Q11999
median2005
Q32011
95-th percentile2015
Maximum2016
Range100
Interquartile range (IQR)12

Descriptive statistics

Standard deviation12.474599
Coefficient of variation (CV)0.0062296043
Kurtosis7.4392126
Mean2002.4705
Median Absolute Deviation (MAD)6
Skewness-2.2922733
Sum9882192
Variance155.61562
MonotonicityNot monotonic
2025-05-08T10:08:16.963521image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2009 260
 
5.3%
2014 252
 
5.1%
2006 239
 
4.8%
2013 237
 
4.8%
2010 230
 
4.7%
2015 226
 
4.6%
2011 225
 
4.6%
2008 225
 
4.6%
2012 221
 
4.5%
2005 221
 
4.5%
Other values (81) 2599
52.7%
ValueCountFrequency (%)
1916 1
< 0.1%
1920 1
< 0.1%
1925 1
< 0.1%
1927 1
< 0.1%
1929 2
< 0.1%
1930 1
< 0.1%
1932 1
< 0.1%
1933 2
< 0.1%
1934 1
< 0.1%
1935 1
< 0.1%
ValueCountFrequency (%)
2016 106
2.1%
2015 226
4.6%
2014 252
5.1%
2013 237
4.8%
2012 221
4.5%
2011 225
4.6%
2010 230
4.7%
2009 260
5.3%
2008 225
4.6%
2007 204
4.1%

actor_2_facebook_likes
Real number (ℝ)

High correlation  Zeros 

Distinct914
Distinct (%)18.6%
Missing10
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1676.8138
Minimum0
Maximum137000
Zeros53
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2025-05-08T10:08:17.187248image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27
Q1282
median599
Q3920
95-th percentile11000
Maximum137000
Range137000
Interquartile range (IQR)638

Descriptive statistics

Standard deviation4081.2264
Coefficient of variation (CV)2.4339175
Kurtosis252.15639
Mean1676.8138
Median Absolute Deviation (MAD)320
Skewness9.7926915
Sum8258308
Variance16656409
MonotonicityNot monotonic
2025-05-08T10:08:17.519518image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 306
 
6.2%
11000 111
 
2.2%
2000 100
 
2.0%
3000 75
 
1.5%
0 53
 
1.1%
10000 47
 
1.0%
14000 41
 
0.8%
13000 40
 
0.8%
826 37
 
0.7%
4000 34
 
0.7%
Other values (904) 4081
82.7%
ValueCountFrequency (%)
0 53
1.1%
2 13
 
0.3%
3 12
 
0.2%
4 11
 
0.2%
5 10
 
0.2%
6 7
 
0.1%
7 2
 
< 0.1%
8 9
 
0.2%
9 12
 
0.2%
10 9
 
0.2%
ValueCountFrequency (%)
137000 1
 
< 0.1%
29000 1
 
< 0.1%
27000 2
 
< 0.1%
25000 3
 
0.1%
23000 6
0.1%
22000 11
0.2%
21000 4
 
0.1%
20000 6
0.1%
19000 7
0.1%
18000 9
0.2%

imdb_score
Real number (ℝ)

Distinct77
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4175887
Minimum1.6
Maximum9.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2025-05-08T10:08:17.809963image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile4.3
Q15.8
median6.5
Q37.2
95-th percentile8
Maximum9.3
Range7.7
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.1146001
Coefficient of variation (CV)0.17367896
Kurtosis0.95904943
Mean6.4175887
Median Absolute Deviation (MAD)0.7
Skewness-0.75739696
Sum31670.8
Variance1.2423335
MonotonicityNot monotonic
2025-05-08T10:08:18.086909image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.7 219
 
4.4%
6.6 197
 
4.0%
7.2 192
 
3.9%
6.5 186
 
3.8%
6.4 185
 
3.7%
7 180
 
3.6%
6.8 180
 
3.6%
7.1 178
 
3.6%
6.1 178
 
3.6%
7.3 177
 
3.6%
Other values (67) 3063
62.1%
ValueCountFrequency (%)
1.6 1
 
< 0.1%
1.7 1
 
< 0.1%
1.9 3
0.1%
2 2
< 0.1%
2.1 3
0.1%
2.2 3
0.1%
2.3 3
0.1%
2.4 2
< 0.1%
2.5 2
< 0.1%
2.6 2
< 0.1%
ValueCountFrequency (%)
9.3 1
 
< 0.1%
9.2 1
 
< 0.1%
9.1 1
 
< 0.1%
9 2
 
< 0.1%
8.9 5
 
0.1%
8.8 5
 
0.1%
8.7 10
0.2%
8.6 12
0.2%
8.5 21
0.4%
8.4 23
0.5%

aspect_ratio
Real number (ℝ)

Missing 

Distinct21
Distinct (%)0.5%
Missing307
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean2.1290125
Minimum1.18
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2025-05-08T10:08:18.316744image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1.18
5-th percentile1.78
Q11.85
median2.35
Q32.35
95-th percentile2.35
Maximum16
Range14.82
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.78917646
Coefficient of variation (CV)0.37067722
Kurtosis265.53737
Mean2.1290125
Median Absolute Deviation (MAD)0
Skewness15.221522
Sum9853.07
Variance0.62279948
MonotonicityNot monotonic
2025-05-08T10:08:18.535629image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2.35 2358
47.8%
1.85 1904
38.6%
1.37 100
 
2.0%
1.78 93
 
1.9%
1.66 64
 
1.3%
1.33 43
 
0.9%
2.2 15
 
0.3%
2.39 15
 
0.3%
16 13
 
0.3%
2 4
 
0.1%
Other values (11) 19
 
0.4%
(Missing) 307
 
6.2%
ValueCountFrequency (%)
1.18 1
 
< 0.1%
1.2 1
 
< 0.1%
1.33 43
0.9%
1.37 100
2.0%
1.44 1
 
< 0.1%
1.5 2
 
< 0.1%
1.66 64
1.3%
1.75 3
 
0.1%
1.77 1
 
< 0.1%
1.78 93
1.9%
ValueCountFrequency (%)
16 13
 
0.3%
2.76 3
 
0.1%
2.55 2
 
< 0.1%
2.4 3
 
0.1%
2.39 15
 
0.3%
2.35 2358
47.8%
2.24 1
 
< 0.1%
2.2 15
 
0.3%
2 4
 
0.1%
1.89 1
 
< 0.1%

movie_facebook_likes
Real number (ℝ)

Zeros 

Distinct864
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7594.6075
Minimum0
Maximum349000
Zeros2134
Zeros (%)43.2%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2025-05-08T10:08:18.764137image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median166
Q33000
95-th percentile41000
Maximum349000
Range349000
Interquartile range (IQR)3000

Descriptive statistics

Standard deviation19453.867
Coefficient of variation (CV)2.5615369
Kurtosis40.973992
Mean7594.6075
Median Absolute Deviation (MAD)166
Skewness5.0422086
Sum37479388
Variance3.7845295 × 108
MonotonicityNot monotonic
2025-05-08T10:08:19.000937image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2134
43.2%
1000 107
 
2.2%
11000 81
 
1.6%
10000 79
 
1.6%
12000 61
 
1.2%
13000 58
 
1.2%
2000 54
 
1.1%
15000 53
 
1.1%
14000 48
 
1.0%
16000 46
 
0.9%
Other values (854) 2214
44.9%
ValueCountFrequency (%)
0 2134
43.2%
2 2
 
< 0.1%
3 1
 
< 0.1%
4 4
 
0.1%
5 2
 
< 0.1%
7 3
 
0.1%
8 1
 
< 0.1%
9 3
 
0.1%
10 2
 
< 0.1%
11 2
 
< 0.1%
ValueCountFrequency (%)
349000 1
< 0.1%
199000 1
< 0.1%
197000 1
< 0.1%
191000 1
< 0.1%
190000 1
< 0.1%
175000 1
< 0.1%
166000 1
< 0.1%
165000 1
< 0.1%
164000 1
< 0.1%
153000 1
< 0.1%

Interactions

2025-05-08T10:07:57.407487image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:15.975844image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:18.658456image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:21.218175image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:23.757959image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:27.048052image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:29.901883image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:32.554436image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:35.347111image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:38.265936image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:40.790970image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:43.577375image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:46.217903image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:48.803607image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:51.638675image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:54.364546image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-05-08T10:07:37.142620image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:39.834957image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:42.480644image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:45.217155image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:47.820365image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:50.610767image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:53.316403image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:56.266913image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:59.457277image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:17.828713image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:20.343671image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:22.919900image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:26.165702image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:29.022444image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:31.711146image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:34.488271image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:37.320607image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:39.995949image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:42.650812image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:45.379095image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:47.975753image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:50.789240image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:53.482683image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:56.480433image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:59.617489image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:17.987656image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:20.486294image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:23.100679image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:26.328159image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:29.206159image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:31.865001image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:34.651006image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:37.483947image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:40.142583image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:42.809997image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:45.534993image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:48.128760image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:50.948730image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:53.641765image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:56.698991image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:59.790418image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:18.151435image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:20.649804image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:23.265137image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:26.505975image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:29.386998image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:32.037785image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:34.831389image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:37.649866image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:40.299284image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:42.993091image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:45.707531image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:48.300453image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:51.115967image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:53.815896image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:56.883343image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:59.966491image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:18.308360image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:20.805738image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:23.437673image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:26.680649image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:29.559527image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:32.210636image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:35.001244image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:37.920957image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:40.466861image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:43.156264image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:45.873959image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:48.465560image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:51.293998image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:53.994011image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:57.050878image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:08:00.145155image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:18.473618image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:21.053987image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:23.596897image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:26.874423image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:29.727375image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:32.376859image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:35.167864image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:38.088794image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:40.622054image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:43.316164image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:46.042132image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:48.638968image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:51.464007image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:54.180342image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-08T10:07:57.230742image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-05-08T10:08:19.195448image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
actor_1_facebook_likesactor_2_facebook_likesactor_3_facebook_likesaspect_ratiobudgetcast_total_facebook_likescolorcontent_ratingdirector_facebook_likesdurationfacenumber_in_postergrossimdb_scorelanguagemovie_facebook_likesnum_critic_for_reviewsnum_user_for_reviewsnum_voted_userstitle_year
actor_1_facebook_likes1.0000.7630.6550.1800.4240.9560.0000.0000.1640.2250.1150.4110.0640.0000.1100.3690.3830.4500.078
actor_2_facebook_likes0.7631.0000.8590.1560.4160.8390.0000.0000.1380.1890.1060.423-0.0170.0000.1020.3170.3480.4010.070
actor_3_facebook_likes0.6550.8591.0000.1200.3860.7690.0000.0000.1160.1540.1120.408-0.0570.0000.0890.2680.3180.3560.049
aspect_ratio0.1800.1560.1201.0000.3030.1810.0000.1180.0890.2560.0370.154-0.0190.0000.0850.2590.1670.1890.279
budget0.4240.4160.3860.3031.0000.4470.0000.0000.2000.3520.0270.685-0.0330.5770.1080.4680.5240.5730.042
cast_total_facebook_likes0.9560.8390.7690.1810.4471.0000.0000.0000.1690.2280.1310.4460.0360.0000.1160.3690.3920.4610.081
color0.0000.0000.0000.0000.0000.0001.0000.2510.0240.1050.0000.0290.1870.1220.0000.0000.0660.0000.401
content_rating0.0000.0000.0000.1180.0000.0000.2511.0000.0110.1090.0000.0870.0610.0730.0350.0990.0260.0240.310
director_facebook_likes0.1640.1380.1160.0890.2000.1690.0240.0111.0000.2050.0120.2130.1360.1560.0410.2400.2540.275-0.042
duration0.2250.1890.1540.2560.3520.2280.1050.1090.2051.0000.0500.2930.3660.1480.1040.2830.3700.370-0.090
facenumber_in_poster0.1150.1060.1120.0370.0270.1310.0000.0000.0120.0501.000-0.012-0.0930.040-0.010-0.070-0.093-0.0400.065
gross0.4110.4230.4080.1540.6850.4460.0290.0870.2130.293-0.0121.0000.1170.0000.1150.5760.6650.736-0.036
imdb_score0.064-0.017-0.057-0.019-0.0330.0360.1870.0610.1360.366-0.0930.1171.0000.0220.1320.3270.3470.411-0.160
language0.0000.0000.0000.0000.5770.0000.1220.0730.1560.1480.0400.0000.0221.0000.0000.0000.0240.0000.000
movie_facebook_likes0.1100.1020.0890.0850.1080.1160.0000.0350.0410.104-0.0100.1150.1320.0001.0000.2790.1600.2030.261
num_critic_for_reviews0.3690.3170.2680.2590.4680.3690.0000.0990.2400.283-0.0700.5760.3270.0000.2791.0000.7890.8210.281
num_user_for_reviews0.3830.3480.3180.1670.5240.3920.0660.0260.2540.370-0.0930.6650.3470.0240.1600.7891.0000.899-0.136
num_voted_users0.4500.4010.3560.1890.5730.4610.0000.0240.2750.370-0.0400.7360.4110.0000.2030.8210.8991.000-0.041
title_year0.0780.0700.0490.2790.0420.0810.4010.310-0.042-0.0900.065-0.036-0.1600.0000.2610.281-0.136-0.0411.000

Missing values

2025-05-08T10:08:00.535824image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-08T10:08:01.311825image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-08T10:08:01.801779image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

colordirector_namenum_critic_for_reviewsdurationdirector_facebook_likesactor_3_facebook_likesactor_2_nameactor_1_facebook_likesgrossgenresactor_1_namemovie_titlenum_voted_userscast_total_facebook_likesactor_3_namefacenumber_in_posterplot_keywordsmovie_imdb_linknum_user_for_reviewslanguagecountrycontent_ratingbudgettitle_yearactor_2_facebook_likesimdb_scoreaspect_ratiomovie_facebook_likes
0ColorJames Cameron723.0178.00.0855.0Joel David Moore1000.0760505847.0action|adventure|fantasy|sci-fiCCH PounderAvatar8862044834Wes Studi0.0avatar|future|marine|native|paraplegichttp://www.imdb.com/title/tt0499549/?ref_=fn_tt_tt_13054.0englishusaPG-13237000000.02009.0936.07.91.7833000
1ColorGore Verbinski302.0169.0563.01000.0Orlando Bloom40000.0309404152.0action|adventure|fantasyJohnny DeppPirates of the Caribbean: At World's End47122048350Jack Davenport0.0goddess|marriage ceremony|marriage proposal|pirate|singaporehttp://www.imdb.com/title/tt0449088/?ref_=fn_tt_tt_11238.0englishusaPG-13300000000.02007.05000.07.12.350
2ColorSam Mendes602.0148.00.0161.0Rory Kinnear11000.0200074175.0action|adventure|thrillerChristoph WaltzSpectre27586811700Stephanie Sigman1.0bomb|espionage|sequel|spy|terroristhttp://www.imdb.com/title/tt2379713/?ref_=fn_tt_tt_1994.0englishukPG-13245000000.02015.0393.06.82.3585000
3ColorChristopher Nolan813.0164.022000.023000.0Christian Bale27000.0448130642.0action|thrillerTom HardyThe Dark Knight Rises1144337106759Joseph Gordon-Levitt0.0deception|imprisonment|lawlessness|police officer|terrorist plothttp://www.imdb.com/title/tt1345836/?ref_=fn_tt_tt_12701.0englishusaPG-13250000000.02012.023000.08.52.35164000
4ColorAndrew Stanton462.0132.0475.0530.0Samantha Morton640.073058679.0action|adventure|sci-fiDaryl SabaraJohn Carter2122041873Polly Walker1.0alien|american civil war|male nipple|mars|princesshttp://www.imdb.com/title/tt0401729/?ref_=fn_tt_tt_1738.0englishusaPG-13263700000.02012.0632.06.62.3524000
5ColorSam Raimi392.0156.00.04000.0James Franco24000.0336530303.0action|adventure|romanceJ.K. SimmonsSpider-Man 338305646055Kirsten Dunst0.0sandman|spider man|symbiote|venom|villainhttp://www.imdb.com/title/tt0413300/?ref_=fn_tt_tt_11902.0englishusaPG-13258000000.02007.011000.06.22.350
6ColorNathan Greno324.0100.015.0284.0Donna Murphy799.0200807262.0adventure|animation|comedy|family|fantasy|musical|romanceBrad GarrettTangled2948102036M.C. Gainey1.017th century|based on fairy tale|disney|flower|towerhttp://www.imdb.com/title/tt0398286/?ref_=fn_tt_tt_1387.0englishusaPG260000000.02010.0553.07.81.8529000
7ColorJoss Whedon635.0141.00.019000.0Robert Downey Jr.26000.0458991599.0action|adventure|sci-fiChris HemsworthAvengers: Age of Ultron46266992000Scarlett Johansson4.0artificial intelligence|based on comic book|captain america|marvel cinematic universe|superherohttp://www.imdb.com/title/tt2395427/?ref_=fn_tt_tt_11117.0englishusaPG-13250000000.02015.021000.07.52.35118000
8ColorDavid Yates375.0153.0282.010000.0Daniel Radcliffe25000.0301956980.0adventure|family|fantasy|mysteryAlan RickmanHarry Potter and the Half-Blood Prince32179558753Rupert Grint3.0blood|book|love|potion|professorhttp://www.imdb.com/title/tt0417741/?ref_=fn_tt_tt_1973.0englishukPG250000000.02009.011000.07.52.3510000
9ColorZack Snyder673.0183.00.02000.0Lauren Cohan15000.0330249062.0action|adventure|sci-fiHenry CavillBatman v Superman: Dawn of Justice37163924450Alan D. Purwin0.0based on comic book|batman|sequel to a reboot|superhero|supermanhttp://www.imdb.com/title/tt2975590/?ref_=fn_tt_tt_13018.0englishusaPG-13250000000.02016.04000.06.92.35197000
colordirector_namenum_critic_for_reviewsdurationdirector_facebook_likesactor_3_facebook_likesactor_2_nameactor_1_facebook_likesgrossgenresactor_1_namemovie_titlenum_voted_userscast_total_facebook_likesactor_3_namefacenumber_in_posterplot_keywordsmovie_imdb_linknum_user_for_reviewslanguagecountrycontent_ratingbudgettitle_yearactor_2_facebook_likesimdb_scoreaspect_ratiomovie_facebook_likes
4925ColorAsh Baron-Cohen10.098.03.0152.0Stanley B. Herman789.00.0crime|dramaPeter GreeneBang4381186James Noble1.0corruption|homeless|homeless man|motorcycle|urban legendhttp://www.imdb.com/title/tt0109266/?ref_=fn_tt_tt_114.0englishusaNot Rated0.01995.0194.06.4NaN20
4926ColorShane Carruth143.077.0291.08.0David Sullivan291.0424760.0drama|sci-fi|thrillerShane CarruthPrimer72639368Casey Gooden0.0changing the future|independent film|invention|nonlinear timeline|time travelhttp://www.imdb.com/title/tt0390384/?ref_=fn_tt_tt_1371.0englishusaPG-137000.02004.045.07.01.8519000
4927ColorNeill Dela Llana35.080.00.00.0Edgar Tancangco0.070071.0thrillerIan GamazonCavite5890Quynn Ton0.0jihad|mindanao|philippines|security guard|squatterhttp://www.imdb.com/title/tt0428303/?ref_=fn_tt_tt_135.0englishphilippinesNot Rated7000.02005.00.06.3NaN74
4928ColorRobert Rodriguez56.081.00.06.0Peter Marquardt121.02040920.0action|crime|drama|romance|thrillerCarlos GallardoEl Mariachi52055147Consuelo Gómez0.0assassin|death|guitar|gun|mariachihttp://www.imdb.com/title/tt0104815/?ref_=fn_tt_tt_1130.0spanishusaR7000.01992.020.06.91.370
4929ColorAnthony ValloneNaN84.02.02.0John Considine45.00.0crime|dramaRichard JewellThe Mongol King3693Sara Stepnicka0.0jewell|mongol|nostradamus|stepnicka|vallonehttp://www.imdb.com/title/tt0430371/?ref_=fn_tt_tt_11.0englishusaPG-133250.02005.044.07.8NaN4
4930ColorEdward Burns14.095.00.0133.0Caitlin FitzGerald296.04584.0comedy|dramaKerry BishéNewlyweds1338690Daniella Pineda1.0written and directed by cast memberhttp://www.imdb.com/title/tt1880418/?ref_=fn_tt_tt_114.0englishusaNot Rated9000.02011.0205.06.4NaN413
4931ColorScott Smith1.087.02.0318.0Daphne Zuniga637.00.0comedy|dramaEric MabiusSigned Sealed Delivered6292283Crystal Lowe2.0fraud|postal worker|prison|theft|trialhttp://www.imdb.com/title/tt3000844/?ref_=fn_tt_tt_16.0englishcanadaNot Rated0.02013.0470.07.7NaN84
4932ColorBenjamin Roberds13.076.00.00.0Maxwell Moody0.00.0drama|horror|thrillerEva BoehnkeA Plague So Pleasant380David Chandler0.0NaNhttp://www.imdb.com/title/tt2107644/?ref_=fn_tt_tt_13.0englishusaNot Rated1400.02013.00.06.3NaN16
4933ColorDaniel Hsia14.0100.00.0489.0Daniel Henney946.010443.0comedy|drama|romanceAlan RuckShanghai Calling12552386Eliza Coupe5.0NaNhttp://www.imdb.com/title/tt2070597/?ref_=fn_tt_tt_19.0englishusaPG-130.02012.0719.06.32.35660
4934ColorJon Gunn43.090.016.016.0Brian Herzlinger86.085222.0documentaryJohn AugustMy Date with Drew4285163Jon Gunn0.0actress name in title|crush|date|four word title|video camerahttp://www.imdb.com/title/tt0378407/?ref_=fn_tt_tt_184.0englishusaPG1100.02004.023.06.61.85456

Duplicate rows

Most frequently occurring

colordirector_namenum_critic_for_reviewsdurationdirector_facebook_likesactor_3_facebook_likesactor_2_nameactor_1_facebook_likesgrossgenresactor_1_namemovie_titlenum_voted_userscast_total_facebook_likesactor_3_namefacenumber_in_posterplot_keywordsmovie_imdb_linknum_user_for_reviewslanguagecountrycontent_ratingbudgettitle_yearactor_2_facebook_likesimdb_scoreaspect_ratiomovie_facebook_likes# duplicates
0Black and WhiteGeorge A. Romero284.096.00.056.0Duane Jones125.00.0drama|horror|mysteryJudith O'DeaNight of the Living Dead87978403S. William Hinzman5.0cemetery|farmhouse|radiation|running out of gas|zombiehttp://www.imdb.com/title/tt0063350/?ref_=fn_tt_tt_1580.0englishusaUnrated114000.01968.0108.08.01.8502
1Black and WhiteYimou Zhang283.080.0611.0576.0Tony Chiu Wai Leung5000.084961.0action|adventure|historyJet LiHero1494146229Maggie Cheung4.0china|flying|king|palace|swordhttp://www.imdb.com/title/tt0299977/?ref_=fn_tt_tt_1841.0mandarinchinaPG-1331000000.02002.0643.07.92.3502
2ColorAlbert Hughes208.0122.0117.0140.0Jason Flemyng40000.031598308.0horror|mystery|thrillerJohnny DeppFrom Hell12476541636Ian Richardson1.0freemason|jack the ripper|opium|prostitute|victorian erahttp://www.imdb.com/title/tt0120681/?ref_=fn_tt_tt_1541.0englishusaR35000000.02001.01000.06.82.3502
3ColorAngelina Jolie Pitt322.0137.011000.0465.0Jack O'Connell769.0115603980.0biography|drama|sport|warFinn WittrockUnbroken1035892938Alex Russell0.0emaciation|male nudity|plane crash|prisoner of war|torturehttp://www.imdb.com/title/tt1809398/?ref_=fn_tt_tt_1351.0englishusaPG-1365000000.02014.0698.07.22.35350002
4ColorBill Condon322.0115.0386.012000.0Kristen Stewart21000.0292298923.0adventure|drama|fantasy|romanceRobert PattinsonThe Twilight Saga: Breaking Dawn - Part 218539459177Taylor Lautner3.0battle|friend|super strength|vampire|visionhttp://www.imdb.com/title/tt1673434/?ref_=fn_tt_tt_1329.0englishusaPG-13120000000.02012.017000.05.52.35650002
5ColorBrett Ratner245.0101.0420.0467.0Rufus Sewell12000.072660029.0action|adventureDwayne JohnsonHercules11568716235Ingrid Bolsø Berdal0.0army|greek mythology|hercules|king|mercenaryhttp://www.imdb.com/title/tt1267297/?ref_=fn_tt_tt_1269.0englishusaPG-13100000000.02014.03000.06.02.35210002
6ColorBruce McCulloch52.085.054.0455.0Megan Mullally985.013973532.0comedy|crimeMartin StarrStealing Harvard112113065Chris Penn1.0black humor|crying during sex|harvard|humor|man with glasseshttp://www.imdb.com/title/tt0265808/?ref_=fn_tt_tt_192.0englishusaPG-1325000000.02002.0637.05.11.852152
7ColorDan CurtisNaN99.045.0224.0Campbell Scott1000.00.0fantasy|romanceJennifer Jason LeighThe Love Letter14652166Estelle Parsons1.0antique|desk|letter|love|time travelhttp://www.imdb.com/title/tt0140340/?ref_=fn_tt_tt_156.0englishusaUnrated0.01998.0393.07.41.335152
8ColorDanny Boyle393.0101.00.0888.0Spencer Wilding3000.02319187.0crime|drama|mystery|thrillerRosario DawsonTrance926405056Tuppence Middleton0.0amnesia|criminal|heist|hypnotherapy|lost paintinghttp://www.imdb.com/title/tt1924429/?ref_=fn_tt_tt_1212.0englishukR20000000.02013.01000.07.02.35230002
9ColorDavid Hewlett8.088.0686.0405.0David Hewlett847.00.0comedyChristopher JudgeA Dog's Breakfast32622364Paul McGillion2.0dog|vegetarianhttp://www.imdb.com/title/tt0796314/?ref_=fn_tt_tt_146.0englishcanadaNot Rated120000.02007.0686.07.01.783772